Using prior information on the intraclass correlation coefficient to analyze data from unreplicated and under-replicated experiments

by Perrett, Jamis J.

Abstract (Summary)
Many studies are performed on units that cannot be replicated due to cost or other restrictions.

There often is an abundance of subsampling to estimate the within unit component of

variance, but what is needed for statistical tests is an estimate of the between unit component

of variance. There is evidence to suggest that the ratio of the between component of variance

to the total variance will remain relatively constant over a range of studies of similar types.

Moreover, in many cases this intraclass correlation, which is the ratio of the between unit variance to the total variance, will be relatively small, often 0.1 or less. Such situations exist in education, agriculture, and medicine to name a few.

The present study discusses how to use such prior information on the intraclass correlation

coefficient (ICC) to obtain inferences about differences among treatments in the face of no

replication. Several strategies that use the ICC are recommended for different situations and

various designs. Their properties are investigated. Work is extended to under-replicated

experiments. The work has a Bayesian flavor but avoids the full Bayesian analysis, which has

computational complexities and the potential for lack of acceptance among many applied

researchers. This study compares the prior information ICC methods with traditional

methods. Situations are suggested in which prior information ICC methods are preferable to

traditional methods and those in which the traditional methods are preferable.

Bibliographical Information:


School:Kansas State University

School Location:USA - Kansas

Source Type:Master's Thesis

Keywords:intraclass correlation coefficient unreplicated experiments under replicated prior information unit of analysis group means statistics 0463 agriculture general 0473 education 0515


Date of Publication:01/01/2004

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